Conventional search engines compare input search terms against metadata, to identify displayable results. Some search processes also allow for refined searching in input terms, against particular identified types of metadata. For example, during a search query at a search engine, a user may be able to enter either a word string, e.g. “serial number”, or a corresponding abbreviation, e.g. “SN”, to indicate that subsequent search terms should be applied against serial number metadata. Furthermore, some conventional search engines permit comparison of input search terms against full or partial text.
When applying conventional search technology, users typically obtain several pages of search results for any given search query, necessitating an extended period of review. For example, a common problem which is often encountered with conventional search queries is that the found set of matching sites or information sources, or documents is often too large, i.e. too broad, such as if too few search terms are entered within a search string, or if the search terms are too general. A user must often either manually browse through a large number of found sources to find relevant sites, or must perform a different search, typically having different terms and/or additional terms, in the hopes of more accurately finding the desired sites and/or information.
A similar problem that is also encountered with conventional search inquires is that the found set of matching sites, information sources, or documents is often too small, i.e. too narrow, such as if too many search terms are entered within a string, or if the search terms are too narrow in scope. A user is then typically required to perform one or more subsequent searches, typically having different terms and/or fewer terms, in the hopes of finding a larger found set of desired sites and information.
Several structures and methods have been described for the searching and retrieval of information in a network environment.
J. Breese and C. Kadie, Methods and Apparatus for Tuning a Match Between Entities Having Attributes , U.S. Pat. No. 6,144,964, describe a matching of “entities having attributes, some of which have associated values. The values of the attributes may be adjusted based on number of entities that have values for a particular attribute so that the values decrease as the number increases. The attributes of the entities may b e harmonized and provided with default values so that entities being matched have common attributes defined by the union of the attributes of the entities being matched. The attributes of the entities may be expanded and provided with default values so that the entities being matched have attributes that neither had originally. Match values may be normalized to provide a weight value which may be used to predict an attribute value of a new entity based on known attribute values of known entities. The weight values may be tuned such that relatively high weights are amplified and relatively low weights are suppressed.”
B. Hazlehurst, S. Burke, and K. Nybakken, Intelligent Query System for Automatically Indexing in a Database and Automatically Categorizing Users, U.S. Pat. No. 6,289,353 B1, describe a system which “develops multiple information spaces in which different types of real-world objects (e.g., documents, users, products) can be represented. Machine learning techniques are used to facilitate automated emergence of information spaces in which objects are represented as vectors of real numbers. The system then delivers information to users based upon similarity measures applied to the representation of the objects in these information spaces. The system simultaneously classifies documents, users, products, and other objects. Documents are managed by collators that act as classifiers of overlapping portions of the database of documents. Collators evolve to meet the demands for information delivery expressed by user feedback. Liaisons act on the behalf of users to elicit information from the population of collators. This information is then presented to users upon logging into the system via Internet or another communication channel. Mites handle incoming documents from multiple information sources (e.g., in-house editorial staff, third-party news feeds, large databases, World Wide Web spiders) and feed documents to those collators which provide a good fit for the new documents.”
V. Berstis and H. Rodriguez, Blocking Saves to Web Browser Cache Based on Content Rating, U.S. Pat. No. 6,510,458 B1, describe a process in which a “user sets preference parameters that filter web page contents from being stored in the cache.
The preferences relate to the web page's contents and attributes. Before caching the web page, the contents and attributes of the web page are filtered solely as a function of the web browser. Cache filters take a variety of forms, such as ratings filters, web page identifier filters, and key word filters, which scan accessed contents of a web page for user selected terms. The filtered web page is then blocked from entry in the browser's cache based on the filtering process. Conversely, a user sets preference parameters that filter web page contents to override the block from cache preferences. The browser responds by storing the filtered web pages that were previously designated as web pages not to be cached.”
Other structures and methods for the searching and retrieval of information include: Y. Freund et al., An Efficient Boosting Algorithm for Combining Preferences, AT&T Labs, MIT Laboratory for Computer Science; J. Shavlik et al., Building Intelligent Agents for Web-Based Tasks: A Theory-Refinement Approach, University of Wisconsin-Madison; and J. Shavlik, et al., Intelligent Agents for Web-based Tasks: An Advice-Taking Approach, University of Wisconsin-Madison.
Several other structures and methods provide background information in regard to the search and retrieval of information, such as: European Patent Application No. EP 1 288 795 A1, Query systems; D. Reed, P. Heymann, S. Mushero, K. Jones, J. Oberlander, and D. Banay, Computer, Based Communication System and Method Using Metadata Defining a Control Structure, U.S. Pat. No. 5,862,325; B. Hazlehurst, S. Burke, and K. Nybakken, Intelligent Query System for Automatically Indexing Information in a Database and Automatically Categorizing Users, U.S. Pat. No. 5,974,412; J. Breese and C. Kadie, Methods and Apparatus for Matching Entities and for Predicting an Attribute of an Entity Based on an Attribute Frequency Value, U.S. Pat. No. 6,018,738; D. Donoho, D. Hindawi, and L. Lippincott, Method and Apparatus for Computed Relevance Messaging, U.S. Pat. No. 6,256,664 B1; D. Donoho, D. Hindawi, and L. Lippincott, Inspector for Computed Relevance Messaging, U.S. Pat. No. 6,263,362 B1; A. Lang and D. Kosak, Integrated Collaborative/Content-Based Filter Structure Employing Selectively Shared, Content-Based Profile Data to Evaluate Information Entities in a Massive Information Network, U.S. Pat. No. 6,308,175 B1, A. Lang and D. Kosak, Collaborative/Adaptive Search Engine, U.S. Pat. No. 6,314,420 B1; J. Breese and C. Kadie, Methods and Apparatus, Using Expansion Attributes Having Default, Values, for Matching Entities and Predicting an Attribute of an Entity, U.S. Pat. No. 6,345,264 B1; D. Reed, P. Heymann, S. Mushero, K. Jones, J. Oberlander, and D. Banay, Computer-Based Communication System and Method Using Metadata Defining a Control-Structure, U.S. Pat. No. 6,345,288 B1; J. Breese and C. Kadie, Method and Apparatus, Using Attribute Set Harmonization and Default Attribute Values, for Matching Entities and Predicting an Attribute of an Entity, U.S. Pat. No. 6,353,813 B1; D. Donoho, D. Hindawi, and L. Lippincott, Relevance Clause for Computed Relevance Messaging, U.S. Pat. No. 6,356,936 B1; E. Steeg, Coincidence Detection Method, Products and Apparatus, U.S. Pat. No. 6,493,637 B1; System and Method for Data Collection, Evaluation Information Generation, And Presentation, U.S. Pat. No. 6,539,392 B1; Baudisch, P.; The Profile Editor: Designing a Direct Manipulative Tool for Assembling Profiles; Institute for Integrated Information and Publication Systems IPSI, German National Research Center for Information Technology GMD, Germany; J. Budzik et al.; User Interactions with Everyday Applications as Context for Just-in-time Information Access; Intelligent Information Laboratory, Northwestern University; J. Budzik et al.; Watson: Anticipating and Contextualizing Information Needs; Northwestern University; E. Glover et al.; Improving Category Specific Web Search by Learning Query Modifications; NEC Research Institute, Princeton, N.J., EECS Department, University of Michigan, Ann Arbor, Mich., Information Sciences and Technology, Pennsylvania State University; Pazzani et al., A Framework for Collaborative. Content-Based and Demographic Filtering; Department of Information and Computer Science, University of California, Irvine; T. Bauer et al.; Real Time User Context Modeling for Information Retrieval Agents, Computer Science Department, Indiana University; J. Shavlik et al.; An Instructable, Adaptive Interface for Discovery and Monitoring Information on the World-Wide Web; University of Wisconsin-Madison; J. Budzik et al.; Watson: An Infrastructure for Providing Task-Relevant. Just-In-Time Information: Department of Computer Science, Northwestern University; and D. Nahl, Ethnography Of Novices'First Use Of Web Search Engines: Affective Control In Cognitive Processing; Internet Reference Services Quarterly, vol. 3, no. 2, p. 51-72, 1998.
It would be advantageous to provide a system and an associated method which provides an enhancement to a search system, wherein the results from the search engine are refined or reorganized, based upon information from an identified secondary source. The development of such a search enhancement system would constitute a major technological advance.
It would also be advantageous to provide a system and an associated method which provides an enhancement to a search system, wherein information from an identified secondary source is integrated with a search query, such that results from the search engine are refined or organized, based upon the information from the identified secondary source. The development of such a search enhancement system would constitute a major technological advance.
In addition to search parameters which may be unique to a particular search, there are often parameters that are commonly relevant for a plurality of searches, such as relating to personalized information regarding the user or to similarities between the subject matter of a search. A user is often required to repeatedly input such parameters, along with other parameters that are unique to search.
Several structures and methods have been described for the searching and sorting of information, based on relevance, personal information, or profiles.
J. Driscoll, Method and System for Searching for Relevant Documents from a Text Database Collection, Using Statistical Ranking, Relevancy Feedback and Small Pieces of Text, U.S. Pat. No. 5,642,502, describes a search system and method “for retrieving relevant documents from a text data base collection comprised of patents, medical and legal documents, journals, news stories and the like. Each small piece of text within the documents such as a sentence, phrase and semantic unit in the data base is treated as a document. Natural language queries are used to search for relevant documents from the data base. A first search query creates a selected group of documents. Each word in both the search query and in the documents are given weighted values. Combining the weighted values creates similarity values for each document which are then ranked according to their relevant importance to the search query. A user reading and passing through this ranked list checks off which documents are relevant or not. Then the system automatically causes the original search query to be updated into a second search query which can include the same words, less words or different words than the first search query. Words in the second search query can have the same or different weights compared to the first search query. The system automatically searches the text data base and creates a second group of documents, which as a minimum does not include at least one of the documents found in the first group. The second group can also be comprised of additional documents not found in the first group. The ranking of documents in the second group is different than the first ranking such that the more relevant documents are found closer to the top of the list.”
T. Gerace, Method and Apparatus for Determining Behavioral Profile of a Computer User, U.S. Pat. No. 5,848,396, describes a computer network method and apparatus which “provides targeting of appropriate audience based on psychographic or behavioral profiles of end users. The psychographic profile is formed by recording computer activity and viewing habits of the end user. Content of categories of interest and display format in each category are revealed by the psychographic profile, based on user viewing of agate information. Using the profile (with or without additional user demographics), advertisements are displayed to appropriately selected users. Based on regression analysis of recorded responses of a first set of users viewing the advertisements, the target user profile is refined. Viewing by and regression analysis of recorded responses of subsequent sets of users continually auto-targets and customizes ads for the optimal end user audience.”
F. Herz, System for Customized Electronic Identification of Desirable Objects, U.S. Pat. No. 6,029,195, describes “customized electronic identification of desirable objects, such as news articles, in an electronic media environment, and in particular to a system that automatically constructs both a “target profile” for each target object in the electronic media based, for example, on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a “target profile interest summary” for each user, which target profile interest summary describes the user's interest level in various types of target objects. The system then evaluates the target profiles against the users' target profile interest summaries to generate a user-customized rank ordered listing of target objects most likely to be of interest to each user so that the user can select from among these potentially relevant target objects, which were automatically selected by this system from the plethora of target objects that are profiled on the electronic media. Users' target profile interest summaries can be used to efficiently organize the distribution of information in a large scale system consisting of many users interconnected by means of a communication network. Additionally, a cryptographically-based pseudonym proxy server is provided to ensure the privacy of a user's target profile interest summary, by giving the user control over the ability of third parties to access this summary and to identify or contact the user.”
A. Lang and D. Kosak, Collaborative/Adaptive Search Engine, U.S. Pat. No. 6,314,420 B1, describe a search engine system “for a portal site on the internet. The search engine system employs a regular search engine to make one-shot or demand searches for information entities which provide at least threshold matches to user queries. The search engine system also employs a collaborative/content-based filter to make continuing searches for information entities which match existing wire queries and are ranked and stored over time in user-accessible, system wires corresponding to the respective queries. A user feedback system provides collaborative feedback data for integration with content profile data in the operation of the collaborative/content-based filter. A query processor determines whether a demand search or a wire search is made for an input query.”
D. Kravets, L. Chiriac, J. Esakov, and S. Wan, Search Data Processor, U.S. Pat. No. 6,363,377 B1, describe a “tool to be used with a search engine for a information management system includes methods for refining, filtering, and organizing search queries and search results. A query tuner in the tool allows a user to automatically reformulate a query in order to find a reasonable number of matching documents from the search engine by selectively modifying individual search terms to be weaker or stronger and concurrently requesting a plurality of searches, each with a respectively different modified query. The tool also uses a dynamic filter which employs a dynamic set of record tokens to restrict the results of an arbitrary search query to selectively include or exclude records which correspond to the set of record tokens. The tool also includes a results organizer which aids the user in understanding and visualizing a large number of matching documents returned in response to a search query by clustering like items returned from the search. The query tuner, dynamic filter and results organizer may be used individually or in conjunction. The searched information management system may be consolidated or distributed and may span a global information network such as the Internet.”
P. Biffar, Self-Learning and Self-Personalizing Knowledge Search Engine That Delivers Holistic Results, U.S. Pat. No. 6,397,212 B1, describes a search engine which “provides intelligent multi-dimensional searches, in which the search engine always presents a complete, holistic result, and in which the search engine presents knowledge (i.e. linked facts) and not just information (i.e. facts). The search engine is adaptive, such that the search results improve over time as the system learns about the user and develops a user profile. Thus, the search engine is self personalizing, i.e. it collects and analyzes the user history, and/or it has the user react to solutions and learns from such user reactions. The search engine generates profiles, e.g. it learns from all searches of all users and combines the user profiles and patterns of similar users. The search engine accepts direct user feedback to improve the next search iteration One feature of the invention is locking/unlocking, where a user may select specific attributes that are to remain locked while the search engine matches these locked attributes to all unlocked attributes. The user may also specify details about characteristics, provide and/or receive qualitative ratings of an overall result, and introduce additional criteria to the search strategy or select a search algorithm. Additionally, the system can be set up such that it does not require a keyboard and/or mouse interface, e.g. it can operate with a television remote control or other such human interface.”
G. Cullis, Personalized Search Methods, U.S. Pat. No. 6,539,377 B1, describes a “method of organizing information in which the search activity of previous users is monitored and such activity is used to organize articles for future users. Personal data about future users can be used to provide different article rankings depending on the search activity and personal data of the previous users.”
Other structures and methods have been described which provide background information regarding the searching and sorting of information, based on relevance, personal information, or profiles, such as: J. Pitkow et al.; Personalized Search, Communications of the ACM, vol. 45, no. 9, p. 50-5, September 2002; J. McGowan et al., Who Do You Want To Be Today? Web Personae for Personalised Information Access; Adaptive Hypermedia and Adaptive Web-Based Systems. Second International Conference, AH 2002. Proceedings (Lecture Notes in Computer Science Vol. 2347), p. 514-17, 2002; S. Kalajdziski et al.; Intelligent Recommendation in Digital Library, Proceedings of the IASTED International Conference Intelligent Systems and Control, p. 408-12, ACTA Press, Anaheim, Calif., USA, 2001; L. Kerschberg et al., A Semantic Taxonomy-Based Personalizable Meta-Search Agent, Proceedings of the Second International Conference on Web Information Systems Engineering, vol. 1, p. 41-50; IEEE Comput. Soc., Los Alamitos, Calif., USA, 2002; C. Dichev, A Framework for Context-Driven Web Resource Discovery, Modeling and Using Context, Third International and Interdisciplinary Conference, Context 2001, Proceedings (Lecture Notes in Artificial Intelligence, vol. 2116), p. 433-6, Springer-Verlag, Berlin, Germany, 2001; X. Meng et al., Feasibility of Adding Filtering Process in Web Browser to Improve Web Search Accuracy, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA '2000, vol. 4, p. 1809-15, CSREA Press, Athens, Ga., USA, 2000; K. Kim et al., Development of a Personalized Link-Based Search Engine Using Fuzzy Concept Network, Journal of KISS: Computing Practices, vol. 7, no. 3, p. 211-19, Korea Inf. Sci. Soc., June 2001; C. Yang et al., A Hybrid Document Clustering for a Web Agent, Journal of KISS: Software and Applications, vol. 28, no. 5, p. 422-30, Korea Inf. Sci. Soc., May 2001; K. Kim et al.; A Personalized Web Search Engine Using Fuzzy Concept Network with Link Structure; Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), vol. 1, p. 81-6; IEEE Piscataway, N.J., USA; 2001; A. Scime et al.; WebSifter: An Ontology-Based Personalizable Search Agent for the Web: Proceedings 2000 Kyoto International Conference on Digital Libraries: Research and Practice, p. 203-10; IEEE Comput. Soc., Los Alamitos, Calif., USA; 2000; Z. Wei-Feng et al., Personalizing Search Result Using Agent, Mini-Micro Systems, vol. 22, no. 6, p. 724-7, Mini-Micro Syst., China, June 2001; P. Chen et al., An Information Retrieval System Based on a User Profile, Journal of Systems and Software, vol. 54, no. 1, p. 3-8, Elsevier, Sep. 30, 2000; X. Meng et al., Personalize Web Search Using Information On Client's Side, Fifth International Conference for Young Computer Scientists, ICYCS'99, Advances in Computer Science and Technology, vol. 2, p. 985-92; Int. Acad. Publishers, Beijing, China, 1999; P. Chen et al.; A Personalized Information Retrieval System: Computational Intelligence for Modelling, Control and Automation, Intelligent Image Processing, Data Analysis and Information Retrieval (Concurrent Systems Engineering Series, vol. 56), p. 247-53, IOS Press, Amsterdam, Netherlands, 1999; S. Laine-Cruzel et al., Improving Information Retrieval by Combining User Profile and Document Segmentation, Information Processing & Management, vol. 32, no. 3, p. 305-15; Elsevier, May 1996; D. Boley et al., Document Categorization and Query Generation on the World Wide Web Using WebACE; Department of Computer Science and Engineering, University of Minnesota; and A. Pretschner, Ontology Based Personalized Search, Dipl.-Inform., RWTH Aachen, Germany, 1998.
Other documents provide background information regarding advancements in search engine structures and processes, such as: European Patent Application No. EP 1 072 982 A2, Method and System for Similar Word Extraction And Document Retrieval; European Patent Specification No. EP 1 095 326 B1, A Search System and Method for Retrieval of Data, and the Use Thereof in a Search Engine; European Patent Application No. EP 1 284 461 A1, Meta-Document Management System With User Definable Personalities; European Patent Application No. EP 1 288 795 A1, Query systems; A. Lang and D. Kosak, System and Method Employing Individual User Content-Based Data and User Collaborative Feedback Data to Evaluate the Content of an Information Entity in a Large Information Communication Network, U.S. Pat. No. 5,983,214; A. Lang and D. Kosak, Multi-Level Mindpool System Especially Adapted to Provide Collaborative Filter Data for a Large Scale Information Filtering System, U.S. Pat. No. 6,029,161; M. Tso, D. Romrell, And D. Gillespie, System for Distributing Electronic Information to a Targeted Group of Users, U.S. Pat. No. 6,047,327; G. Culliss, Personalized Search Methods, U.S. Pat. No. 6,182,068 B1; A. Lang and D. Kosak, Integrated Collaborative/Content-Based Filter Structure Employing Selectively Shared, Content-Based Profile Data to Evaluate Information Entities in a Massive Information Network; U.S. Pat. No. 6,308,175 B1; D. Chen, Cooperative Topical Servers With Automatic Prefiltering and Routing, U.S. Pat. No. 6,349,307 B1; D. Judd, P. Gauthier, and J. Baldeschwieler, Method and Apparatus for Retrieving Documents Based on Information other than Document Content, U.S. Pat. No. 6,360,215 B1; K. Risvik, Search System and Method for Retrieval of Data, and the Use Thereof in a Search Engine, U.S. Pat. No. 6,377,945 B1; E. Marwell and R. Pines, Personalized Assistance System and Method, U.S. Pat. No. 6,404,884 B 1; A. Weissman and G. Elbaz, Meaning-Based Information Organization and Retrieval, U.S. Pat. No. 6,453,315 B1; J. Lee, L. Morgenstern, M. Pedlaseck, E. Schonberg, and D. Wood, System and Method for Collecting and Analyzing Information About Content Requested in a Network (World Wide Web) Environment, U.S. Pat. No. 6,466,970 B1; S. Edlund, M. Emens, R. Kraft, and P. Yim, Labeling and Describing Search Queries for Reuse, U.S. Pat. No. 6,484,162 B1; J. Zhang and M. Ott, Method and Apparatus for Active Information Discovery and Retrieval, U.S. Pat. No. 6,498,795 B1; L. Nikolovska, J. Martino, and A. Camplin, Search User Interface with Enhanced Accessibility and Ease-Of-Use Features Based on Visual Metaphors, U.S. Pat. No. 6,505,194 B1; M. Bowman-Amuah, Piecemeal Retrieval in an Information Services Patterns Environment, U.S. Pat. No. 6,550,057 B1; Callan, J. et al.; Document Filtering with Inference Networks; Computer Science Department, University of Massachusetts; Goker, A.; Capturing Information Need by Learning User Context; School of Computer and Mathematical Sciences; The Robert Gordon University; Chen, L. et al.; WebMate: A Personal Agent for Browsing and Searching; The Robotics Institute, Carnegie Mellon Institute; Sep. 30, 1997; Cooley, R. et al.; Web Mining: Information and Pattern Discovery on the World Wide Web; Department of Computer Science and Engineering, University of Minnesota; Simons, J.; Using a Semantic User Model to Filter the World Wide Web Proactively; Nijmegen Institute for Cognition and Information, University of Nijmegen, The Netherlands; Tanudjaja, F. et al.; Persona: A Contextualized and Personalized Web Search; Laboratory of Computer Science at MIT, Cambridge, Mass.; Jun. 1, 2001; Yan, T. et al.; SIFT—A Tool for Wide-Area Information Dissemination; Department of Computer Science, Stanford University, Feb. 16, 1995; Bianchi-Berthouze, N.; Mining Multimedia Subjective Feedback; Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, vol. 19, no. 1, p. 43-59; Kluwer Academic Publishers; July 2002; Widyantoro, D. H. et al.; A fuzzy Ontology-Based Abstract Search Engine and Its User Studies; 10th IEEE International Conference on Fuzzy Systems. (Cat. No. 01CH37297), vol. 2, p. 1291-4; IEEE, Piscataway, N.J., USA; 2001; Tanudjaja, F. et al.; Persona: A Contextualized and Personalized Web Search; Proceedings of the 35th Annual Hawaii International Conference on System Sciences, p. 1232-40; IEEE Comput. Soc, Los Alamitos, Calif., USA; 2002; Widyantoro, D. H. et al.; Using Fuzzy Ontology for Query Refinement in a Personalized Abstract Search Engine; Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), vol. 1, p. 610-15; IEEE, Piscataway, N.J., USA; 2001; Ho, M. et al.; A GA-Based Dynamic Personalized Filtering for Internet Search Service on Multi-Search Engine; Canadian Conference on Electrical and Computer Engineering 2001, Conference Proceedings (Cat. No. 01TH8555) vol. 1, p. 271-6; IEEE, Piscataway, N.J., USA; 2001; Pogaenik, M. et al.; Layered Agent System Architecture for Personalized Retrieval of Information from Internet; Signal Processing X Theories and Applications. Proceedings of EUSIPCO 2000. Tenth European Signal Processing Conference, vol. 1, p. 421-4; Tampere Univ. Technology, Tampere, Finland; 2000; Ho, M. et al.; An Agent-Based Personalized Search on a Multi-Search Engine Based on Internet Search Service: Intelligent Data Engineering and Automated—IDEAL 2000, Data Mining, Financial Engineering, and Intelligent Agents, Second International Conference, Proceedings (Lecture Notes in Computer Science Vol.1983), p. 404-9; Springer-Verlag, Berlin, Germany; 2000; Wei-Feng, Z. et al.; Personalizing Search Result Using Agent; Mini-Micro Systems, vol. 22, no. 6, p. 724-7; Mini-Micro Syst., China; Overmeer, M.A.C.J.; My Personal Search Engine; Computer Networks, vol. 31, no. 21, p. 2271-9; Elsevier, Nov. 10, 1999; Pretschner, A. et al.; Ontology Based Personalized Search; Proceedings 11th International Conference on Tools with Artificial Intelligence, p. 391-8; IEEE Comput. Soc., Los Alamitos, Calif., USA, 1999; Lee, E. S. et al.; Agent-Based Support for Personalized Information with Web Search Engines; Design of Computing Systems: Cognitive Considerations. Proceedings of the Seventh International Conference on Human-Computer Interaction (HCl International '97), vol. 2, p. 783-6; Elsevier, Amsterdam, Netherlands, 1997; and Berger, F. C. et al., Personalized Search Support for Networked Document Retrieval Using Link Inference, Database and Expert Systems Applications. 7th International Conference, DEXA '96 Proceedings, p. 802-11, Springer-Verlag, Berlin, Germany, 1996.
It would be advantageous to provide a system and an associated method which provides an enhancement to a search system, wherein a user may specify one or more search parameters, and wherein the user-specified search parameters are integrated into a search query, based on the subject matter of that query. The development of such a search enhancement system would constitute a major technological advance.
As well, it would be advantageous to provide a system and an associated method which provides an enhancement to a search system, wherein a user may specify one or more search parameters, and wherein the user-specified search parameters are integrated into a search query, based on the subject matter of that query, in which the subject matter is either explicitly determined or is implicitly determined, based upon user input. The development of such a search enhancement system would constitute a further technological advance.
Furthermore, it would be advantageous to provide a system and an associated method which provides an enhancement to a search system, in which general search parameters are solicited from a user before a particularized search, and wherein the solicited search parameters are associated with the user and are available for automatic integration into future particularized searches initiated by the user. The development of such a search enhancement system would constitute a further technological advance.
In addition, it would be advantageous to provide a system and an associated method which provides an enhancement to a search system, in which general search parameters are solicited from a user before or after a particularized search, and wherein the solicited search parameters are associated with user selectable editorial content, such as for delivery to the user and/or to other recipients, and are available for automatic integration into future particularized searches, such as initiated by the user or by other recipients that are associated with the user USR, such as a network of friends, family, peers, students, neighbors, people or entities within a zip code region, and/or business associates. The development of such a search enhancement system would constitute a further technological advance.